Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification

TL;DR

This paper introduces 'virtual branching,' an efficient CNN ensemble method for person re-identification that enhances robustness against pose and view variations with minimal additional computational cost.

Contribution

The paper presents a novel virtual branching technique for CNNs that creates effective ensemble representations without significant extra parameters or computation.

Findings

Achieves competitive results on Market-1501, CUHK03, and DukeMTMC-reID datasets.

Enhances robustness to pose, view, and body misalignment in person re-ID.

Requires nearly no additional parameters or computation.

Abstract

In this paper we introduce an ensemble method for convolutional neural network (CNN), called "virtual branching," which can be implemented with nearly no additional parameters and computation on top of standard CNNs. We propose our method in the context of person re-identification (re-ID). Our CNN model consists of shared bottom layers, followed by "virtual" branches, where neurons from a block of regular convolutional and fully-connected layers are partitioned into multiple sets. Each virtual branch is trained with different data to specialize in different aspects, e.g., a specific body region or pose orientation. In this way, robust ensemble representations are obtained against human body misalignment, deformations, or variations in viewing angles, at nearly no any additional cost. The proposed method achieves competitive performance on multiple person re-ID benchmark datasets, including Market-1501, CUHK03, and DukeMTMC-reID.